No Free Lunch: Fundamental Limits of Learning Non-Hallucinating Generative Models

• URL: http://arxiv.org/abs/2410.19217v1
• Date: Thu, 24 Oct 2024 23:57:11 GMT
• Title: No Free Lunch: Fundamental Limits of Learning Non-Hallucinating Generative Models
• Authors: Changlong Wu, Ananth Grama, Wojciech Szpankowski,
• Abstract summary: We develop a theoretical framework to analyze the learnability of non-hallucinating generative models. We show that incorporating inductive biases aligned with the actual facts into the learning process is essential.
• Score: 14.535583931446807
• License:
• Abstract: Generative models have shown impressive capabilities in synthesizing high-quality outputs across various domains. However, a persistent challenge is the occurrence of "hallucinations", where the model produces outputs that are plausible but invalid. While empirical strategies have been explored to mitigate this issue, a rigorous theoretical understanding remains elusive. In this paper, we develop a theoretical framework to analyze the learnability of non-hallucinating generative models from a learning-theoretic perspective. Our results reveal that non-hallucinating learning is statistically impossible when relying solely on the training dataset, even for a hypothesis class of size two and when the entire training set is truthful. To overcome these limitations, we show that incorporating inductive biases aligned with the actual facts into the learning process is essential. We provide a systematic approach to achieve this by restricting the facts set to a concept class of finite VC-dimension and demonstrate its effectiveness under various learning paradigms. Although our findings are primarily conceptual, they represent a first step towards a principled approach to addressing hallucinations in learning generative models.

Related papers

• LLMs Will Always Hallucinate, and We Need to Live With This [1.3810901729134184]
  This work argues that hallucinations in language models are not just occasional errors but an inevitable feature of these systems. It is, therefore, impossible to eliminate them through architectural improvements, dataset enhancements, or fact-checking mechanisms.
  arXiv  Detail & Related papers  (2024-09-09T16:01:58Z)
• Knowledge Overshadowing Causes Amalgamated Hallucination in Large Language Models [65.32990889402927]
  We coin this phenomenon as knowledge overshadowing'' We show that the hallucination rate grows with both the imbalance ratio and the length of dominant condition description. We propose to utilize overshadowing conditions as a signal to catch hallucination before it is produced.
  arXiv  Detail & Related papers  (2024-07-10T20:37:42Z)
• Learning Discrete Concepts in Latent Hierarchical Models [73.01229236386148]
  Learning concepts from natural high-dimensional data holds potential in building human-aligned and interpretable machine learning models. We formalize concepts as discrete latent causal variables that are related via a hierarchical causal model. We substantiate our theoretical claims with synthetic data experiments.
  arXiv  Detail & Related papers  (2024-06-01T18:01:03Z)
• Unfamiliar Finetuning Examples Control How Language Models Hallucinate [75.03210107477157]
  Large language models are known to hallucinate when faced with unfamiliar queries. We find that unfamiliar examples in the models' finetuning data are crucial in shaping these errors. Our work further investigates RL finetuning strategies for improving the factuality of long-form model generations.
  arXiv  Detail & Related papers  (2024-03-08T18:28:13Z)
• Neural Causal Abstractions [63.21695740637627]
  We develop a new family of causal abstractions by clustering variables and their domains. We show that such abstractions are learnable in practical settings through Neural Causal Models. Our experiments support the theory and illustrate how to scale causal inferences to high-dimensional settings involving image data.
  arXiv  Detail & Related papers  (2024-01-05T02:00:27Z)
• Learn to Accumulate Evidence from All Training Samples: Theory and Practice [7.257751371276488]
  Evidential deep learning offers a principled and computationally efficient way to turn a deterministic neural network uncertainty-aware. Existing evidential activation functions create zero evidence regions, which prevent the model to learn from training samples falling into such regions. A deeper analysis of evidential activation functions based on our theoretical underpinning inspires the design of a novel regularizer.
  arXiv  Detail & Related papers  (2023-06-19T18:27:12Z)
• Does Deep Learning Learn to Abstract? A Systematic Probing Framework [69.2366890742283]
  Abstraction is a desirable capability for deep learning models, which means to induce abstract concepts from concrete instances and flexibly apply them beyond the learning context. We introduce a systematic probing framework to explore the abstraction capability of deep learning models from a transferability perspective.
  arXiv  Detail & Related papers  (2023-02-23T12:50:02Z)
• A Theoretical Study of Inductive Biases in Contrastive Learning [32.98250585760665]
  We provide the first theoretical analysis of self-supervised learning that incorporates the effect of inductive biases originating from the model class. We show that when the model has limited capacity, contrastive representations would recover certain special clustering structures that are compatible with the model architecture.
  arXiv  Detail & Related papers  (2022-11-27T01:53:29Z)
• A Scaling Law for Synthetic-to-Real Transfer: A Measure of Pre-Training [52.93808218720784]
  Synthetic-to-real transfer learning is a framework in which we pre-train models with synthetically generated images and ground-truth annotations for real tasks. Although synthetic images overcome the data scarcity issue, it remains unclear how the fine-tuning performance scales with pre-trained models. We observe a simple and general scaling law that consistently describes learning curves in various tasks, models, and complexities of synthesized pre-training data.
  arXiv  Detail & Related papers  (2021-08-25T02:29:28Z)
• Contrastive Learning Inverts the Data Generating Process [36.30995987986073]
  We prove that feedforward models trained with objectives belonging to the commonly used InfoNCE family learn to implicitly invert the underlying generative model of the observed data. Our theory highlights a fundamental connection between contrastive learning, generative modeling, and nonlinear independent component analysis.
  arXiv  Detail & Related papers  (2021-02-17T16:21:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.